Generally, in an Information Technology (IT) Support environment, large volumes of data is generated from various tools like Ticketing tools, Monitoring tools and Quality systems. To identify one or more issues and to identify opportunities for ensuring service improvements for the one or more issues, recurring patterns of data should be analysed. This is a hectic process as the volume of data is large. Some of the fields could be dimension fields having a fixed list of value, while other fields may be unstructured i.e. free text fields entered by users. Analysing the data using existing dimensions such as ticket category, ticket type etc. only provides the explicitly classified data. The result of the analysis of the data using existing dimensions provides only brief details of the issue. Obtaining data related to the issues at another level of detail to identify specific improvement areas through automation, elimination or other means becomes crucial.
Currently, manual analysis is performed using the data of various dimensions in combination with the unstructured text provided by the user. The key patterns are retrieved manually to identify data related to the issues at another level of detail that is not explicitly available. But due to the huge volumes of data and variety of systems involved, it is a very tedious and time consuming process to identify the issues manually. Also, a lot of resources are involved in the manual process. In one of the existing techniques, the tickets are analysed by collecting data associated with one or more parameters/keywords of tickets and determining if a number of tickets associated with the one or more parameters/keywords exceeds a count threshold associated with the one or more parameters. Further in another existing technique, the tickets are analysed which are associated to at least one issue associated with at least one of a product and service. Analysing the received data may include calibrating the one or more modules based on the received data and processing the data based on the calibration. Most of the existing systems are supervised techniques where the system may not learn on its own. Also, the existing systems do not provide data related to the issues at another level of detail which becomes the input in ensuring service improvements and cost optimization. The existing systems are not robust and generic that can be applied to any kind of tickets and do not have the ability to integrate with an array of different platforms with little or no customizations.
Therefore, there is need for a solution which automatically identifies the issues in the tickets using the unstructured data provided by the user. Also, there is need for a solution which is robust and generic.